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Research on data diagnosis method of acoustic array sensor device based on spectrogram

2022-09-14

国网上海市电力公司雷兴等：基于声谱图的声阵列传感器装置数据诊断方法研究

摘要

声阵列传感器装置局部放电检测与超声波法、脉冲电流法等局放检测方法相比，具有非接触、精确定位等优点在电力电缆巡检中得到广泛的应用。但由于声阵列传感器的灵敏度及电缆运行现场干扰的影响，声阵列传感器装置通过PRPD图进行局部放电类型诊断会偶发错误，从而影响电力电缆的运维策略。本文中开发的电力电缆声阵列传感器检测装置，应用等面积多臂螺旋的阵列设计模型与机器学习FFT-CLEAN声源定位识别算法避免了采用单个麦克风的噪声采集系统及常规波束形成算法中的干扰因素，提高了声阵列传感器装置的空间分辨率，并提出了声阵列传感器装置基于声谱图的局部放电类型的分析诊断方法，可有效降低因为声成像装置的分辨率与数字信号时域脉冲等因素造成的系统误判，降低声阵列传感器装置的误报系统率。本文方法选取电力电缆为对象进行测试，实验室验证和现场验证证明了其有效性。

Research on data diagnosis method of acoustic array sensor device based on spectrogram

基于声谱图的声阵列传感器装置数据诊断方法研究

Xing Lei¹, Hang Ji¹, Qiang Xu¹, Ting Ye¹, Shengfu Zhang¹, Chengjun Huang²

(1.State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, P. R. China

2.Shanghai Jiao Tong University, Shanghai 200240, P. R. China )

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Research on data diagnosis method based on acoustic spectro

Abstract

Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method. However, due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference, the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge (PRPD) map might occasionally presents incorrect results, thus affecting the power equipment operation and maintenance strategy. The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean (FFT-CLEAN) sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm, improves the spatial resolution of the acoustic array sensor device, and proposes an acoustic array sensor device based on the acoustic spectrogram. The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal, and reduce the false alarm rate of the acoustic array sensor device. The proposed method is tested by selecting power cables as the object, and its effectiveness is proved by laboratory verification and field verification.

Keywords

Acoustic array sensor device, Acoustic spectrogram, Partial discharge, Power equipment, False alarm rate.

Fig.1 Block diagram of acoustic array sensor device

Fig.2 Data flow of sound source localization system

Fig.3 Acoustic array sensor device array design

Fig.4 Acoustic array response

Fig.5 Algorithms for array spatial resolution enhancement

Fig.6 Sound source identification calculation time curve

Fig.7 PRPD map formation method

Fig.8 Testing circuit of PD experiment

Fig.9 PD models of typical insulation defects Sound source identification calculation time curve

Fig.10 PRPD mapping of typical acoustic array sensor devices for power equipment

Fig.11 Method of forming acoustic spectrogram

Fig.12 Signal framing diagram

Fig.13 Short-time signal plus window schematic

Fig.14 Complex FFT spectrum-based audio information reconstruction method

Fig.15 Comparison of the effect of audio reconstruction based on FFT spectral information

Fig.16 Cable termination partial discharge models (unit: mm)

Fig.17 Experimental circuit for partial discharge detection of defect models

Fig.18 Acoustic spectrograms of cable terminal partial discharge

Fig.19 Detection results of the acoustic array sensor device

Fig.20 PRPD diagram of acoustic array sensor device Sound source identification calculation time curve

Fig.21 Partial discharge type acoustic spectrogram

Fig.22 Disintegration verification

Fig.23 Analysis of diagnostic results of two different spectrograms Sound source identification calculation time curve

本文引文信息

Lei X, Ji H, Xu Q, et al. (2022) Research on data diagnosis method based on acoustic spectrogram of acoustic array sensor device. Global Energy Interconnection, 5(4): 418-433

雷兴，纪航，许强，等 (2022) 基于声谱图的声阵列传感器装置数据诊断方法研究. 全球能源互联网（英文）, 5(4): 418-433

Biographies

Xing Lei

Xing Lei received M.S. degree at North China Electric Power University in 2006, and received Ph.D. degree at Shandong University in 2012. He is working in State Grid Shanghai Municipal Electric Power Company. His research interests include power system automation, equipment maintenance.

Hang Ji

Hang Ji received B.S. degree at North China Electric Power University in 2006. He is working in State Grid Shanghai Municipal Electric Power Company. His research interests include power system automation, equipment management.

Qiang Xu

Qiang Xu received bachelor’s degree at Shanghai Jiaotong University in 2002. He is working in State Grid Shanghai Municipal Electric Power Company. His research interests include power system automation, equipment maintenance.

Ting Ye

Ting Ye received B.S. degree at Fudan University in 2012. He is working in State Grid Shanghai Municipal Electric Power Company. His research interests include power system automation, equipment maintenance.

Shengfu Zhang

Shengfu Zhang received B.S. degree at Fudan University in 2010. He is working in State Grid Shanghai Municipal Electric Power Company. His research interests include power system automation, equipment maintenance.

Chengjun Huang

Chengjun Huang received Ph.D. degree at Shanghai Jiaotong University in 2000. He is the chairman of Power Monitoring and Diagnostic Technology Ltd. San Jose, USA. His research interests include partial discharge detection technology, intelligent power equipment, and condition maintenance technology.

编辑：王彦博

审核：王伟

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Research on data diagnosis method of acoustic array sensor device based on spectrogram

国网上海市电力公司 雷兴等：基于声谱图的声阵列传感器装置数据诊断方法研究

郑重声明

国网上海市电力公司雷兴等：基于声谱图的声阵列传感器装置数据诊断方法研究